In this study, we break away from the long-standing premise in evolutionary game theory that the learning rule is fixed. Instead we ask whether there is a learning rule that helps individuals find more profitable equilibria. To this end, we imagine two individuals interacting in a repeated game. Each individual has its own learning rule for adapting its strategy, with the ultimate goal of achieving a high payoff. As a baseline, we consider a variant of selfish (or “naïve” [1]}) learning. At regular time intervals, a selfish learner compares the performance of its present strategy with the (hypothetical) performance of a slightly perturbed version of its strategy. The learner adopts the perturbed strategy if it yields the higher payoff and discards it otherwise.
